Overview

Brought to you by YData

Dataset statistics

Number of variables9
Number of observations419
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory32.7 KiB
Average record size in memory80.0 B

Variable types

Numeric8
Categorical1

Alerts

age_days has constant value "0.0" Constant
cement_kg is highly overall correlated with compressive_strength_mpaHigh correlation
compressive_strength_mpa is highly overall correlated with cement_kgHigh correlation

Reproduction

Analysis started2025-04-24 12:49:42.486698
Analysis finished2025-04-24 12:49:46.619781
Duration4.13 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

cement_kg
Real number (ℝ)

High correlation 

Distinct277
Distinct (%)66.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.013631669
Minimum-1.5633438
Maximum2.626147
Zeros0
Zeros (%)0.0%
Negative216
Negative (%)51.6%
Memory size6.5 KiB
2025-04-24T07:49:46.666986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1.5633438
5-th percentile-1.1886809
Q1-1.008571
median-0.052066267
Q30.54096667
95-th percentile2.0092015
Maximum2.626147
Range4.1894908
Interquartile range (IQR)1.5495377

Descriptive statistics

Standard deviation0.99958891
Coefficient of variation (CV)-73.328432
Kurtosis-0.27793946
Mean-0.013631669
Median Absolute Deviation (MAD)0.83808946
Skewness0.64259438
Sum-5.7116693
Variance0.99917799
MonotonicityNot monotonic
2025-04-24T07:49:46.740485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4548812469 6
 
1.4%
-1.113786531 5
 
1.2%
-1.056396246 5
 
1.2%
0.4261861046 5
 
1.2%
-1.075526341 5
 
1.2%
0.3114055355 4
 
1.0%
-1.161611768 4
 
1.0%
-1.058309256 4
 
1.0%
-0.9894409143 4
 
1.0%
1.727032554 4
 
1.0%
Other values (267) 373
89.0%
ValueCountFrequency (%)
-1.56334376 1
0.2%
-1.503083961 1
0.2%
-1.429433096 1
0.2%
-1.366303783 1
0.2%
-1.276392337 2
0.5%
-1.26682729 2
0.5%
-1.265870785 1
0.2%
-1.250566709 1
0.2%
-1.247697195 2
0.5%
-1.241001662 1
0.2%
ValueCountFrequency (%)
2.626147012 3
0.7%
2.5429311 1
 
0.2%
2.511366443 1
 
0.2%
2.482671301 1
 
0.2%
2.453976159 1
 
0.2%
2.434846064 2
0.5%
2.396585874 2
0.5%
2.291370353 1
 
0.2%
2.24450162 1
 
0.2%
2.243545115 3
0.7%

scm1_kg
Real number (ℝ)

Distinct186
Distinct (%)44.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.0071251041
Minimum-0.98360743
Maximum3.1133789
Zeros0
Zeros (%)0.0%
Negative207
Negative (%)49.4%
Memory size6.5 KiB
2025-04-24T07:49:46.814177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.98360743
5-th percentile-0.98360743
Q1-0.98360743
median0.080765441
Q30.81637625
95-th percentile1.7160261
Maximum3.1133789
Range4.0969863
Interquartile range (IQR)1.7999837

Descriptive statistics

Standard deviation1.0021438
Coefficient of variation (CV)-140.6497
Kurtosis-0.91201046
Mean-0.0071251041
Median Absolute Deviation (MAD)1.0643729
Skewness0.49564311
Sum-2.9854186
Variance1.0042921
MonotonicityNot monotonic
2025-04-24T07:49:46.885357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.9836074295 173
41.3%
0.6693224869 11
 
2.6%
-0.7670166129 4
 
1.0%
-0.7100190296 4
 
1.0%
1.148102187 4
 
1.0%
0.3387365036 4
 
1.0%
-0.7556170962 3
 
0.7%
1.706678503 3
 
0.7%
1.17090122 3
 
0.7%
1.182300737 3
 
0.7%
Other values (176) 207
49.4%
ValueCountFrequency (%)
-0.9836074295 173
41.3%
-0.9833794392 1
 
0.2%
-0.8582127462 1
 
0.2%
-0.8284600077 1
 
0.2%
-0.8126146796 2
 
0.5%
-0.7875357429 1
 
0.2%
-0.7841158879 1
 
0.2%
-0.7829759362 1
 
0.2%
-0.7670166129 4
 
1.0%
-0.7556170962 3
 
0.7%
ValueCountFrequency (%)
3.11337886 1
0.2%
2.916167222 1
0.2%
2.619779788 1
0.2%
2.496665008 1
0.2%
2.324532307 1
0.2%
2.29945337 1
0.2%
2.240175883 1
0.2%
2.126180717 1
0.2%
2.00534584 1
0.2%
1.980266903 1
0.2%

scm2_kg
Real number (ℝ)

Distinct163
Distinct (%)38.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.013593559
Minimum-0.94928355
Maximum2.0756791
Zeros0
Zeros (%)0.0%
Negative207
Negative (%)49.4%
Memory size6.5 KiB
2025-04-24T07:49:46.964267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.94928355
5-th percentile-0.94928355
Q1-0.94928355
median0.19358089
Q30.87499114
95-th percentile1.6925474
Maximum2.0756791
Range3.0249626
Interquartile range (IQR)1.8242747

Descriptive statistics

Standard deviation1.0018114
Coefficient of variation (CV)73.697508
Kurtosis-1.4297702
Mean0.013593559
Median Absolute Deviation (MAD)1.1428644
Skewness0.32870314
Sum5.6957013
Variance1.0036262
MonotonicityNot monotonic
2025-04-24T07:49:47.037086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.9492835515 202
48.2%
0.7589662096 8
 
1.9%
0.6682626824 7
 
1.7%
1.182249336 4
 
1.0%
0.2449795558 4
 
1.0%
1.212483845 4
 
1.0%
0.4717383736 4
 
1.0%
0.8043179731 3
 
0.7%
0.9857250274 3
 
0.7%
0.8386341409 3
 
0.7%
Other values (153) 177
42.2%
ValueCountFrequency (%)
-0.9492835515 202
48.2%
-0.5795155059 1
 
0.2%
-0.5787596432 1
 
0.2%
-0.5786084706 1
 
0.2%
-0.05736553466 1
 
0.2%
-0.04224828014 1
 
0.2%
0.1240415196 1
 
0.2%
0.1316001469 1
 
0.2%
0.1935808904 1
 
0.2%
0.1996277922 1
 
0.2%
ValueCountFrequency (%)
2.075679079 1
 
0.2%
2.074167353 1
 
0.2%
1.99858108 3
0.7%
1.997069355 1
 
0.2%
1.983463826 1
 
0.2%
1.968346571 1
 
0.2%
1.922994808 1
 
0.2%
1.877643044 1
 
0.2%
1.851943712 1
 
0.2%
1.847408535 2
0.5%

water_kg
Real number (ℝ)

Distinct204
Distinct (%)48.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.013802428
Minimum-3.1755438
Maximum3.3118614
Zeros0
Zeros (%)0.0%
Negative195
Negative (%)46.5%
Memory size6.5 KiB
2025-04-24T07:49:47.108884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-3.1755438
5-th percentile-1.6418746
Q1-0.5831715
median0.10053109
Q30.53043499
95-th percentile1.7165554
Maximum3.3118614
Range6.4874052
Interquartile range (IQR)1.1136065

Descriptive statistics

Standard deviation0.9994831
Coefficient of variation (CV)72.413573
Kurtosis0.84085593
Mean0.013802428
Median Absolute Deviation (MAD)0.51795651
Skewness0.01091078
Sum5.7832172
Variance0.99896647
MonotonicityNot monotonic
2025-04-24T07:49:47.183325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4631006471 28
 
6.7%
0.1367880479 23
 
5.5%
-0.2620384623 11
 
2.6%
0.1523267431 11
 
2.6%
0.1005310924 10
 
2.4%
-1.090768873 10
 
2.4%
2.327744071 9
 
2.1%
1.05875063 9
 
2.1%
0.5148962978 7
 
1.7%
0.8774658525 7
 
1.7%
Other values (194) 294
70.2%
ValueCountFrequency (%)
-3.175543812 1
0.2%
-2.924334907 1
0.2%
-2.903616646 1
0.2%
-2.888077951 1
0.2%
-2.344223619 1
0.2%
-2.230273188 1
0.2%
-2.19142645 1
0.2%
-2.137041017 1
0.2%
-2.126681886 1
0.2%
-2.059347541 1
0.2%
ValueCountFrequency (%)
3.311861434 1
 
0.2%
3.306681869 1
 
0.2%
2.793904927 1
 
0.2%
2.778366232 1
 
0.2%
2.327744071 9
2.1%
1.985892777 1
 
0.2%
1.965174517 2
 
0.5%
1.918558431 1
 
0.2%
1.913378866 2
 
0.5%
1.897840171 1
 
0.2%

additive_kg
Real number (ℝ)

Distinct155
Distinct (%)37.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.011447373
Minimum-1.2987297
Maximum4.6800349
Zeros0
Zeros (%)0.0%
Negative186
Negative (%)44.4%
Memory size6.5 KiB
2025-04-24T07:49:47.254875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1.2987297
5-th percentile-1.2987297
Q1-1.2987297
median0.14397223
Q30.57659712
95-th percentile1.6033849
Maximum4.6800349
Range5.9787646
Interquartile range (IQR)1.8753268

Descriptive statistics

Standard deviation0.99620673
Coefficient of variation (CV)-87.02492
Kurtosis1.1049691
Mean-0.011447373
Median Absolute Deviation (MAD)0.59973322
Skewness0.57960915
Sum-4.7964493
Variance0.99242786
MonotonicityNot monotonic
2025-04-24T07:49:47.326410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.29872965 107
25.5%
0.1866776942 27
 
6.4%
-0.1846741418 17
 
4.1%
0.3723536123 15
 
3.6%
0.001001776188 15
 
3.6%
0.5580295303 15
 
3.6%
0.7437054483 14
 
3.3%
-0.741701896 6
 
1.4%
1.48640912 4
 
1.0%
0.9293813664 4
 
1.0%
Other values (145) 195
46.5%
ValueCountFrequency (%)
-1.29872965 107
25.5%
-0.979367071 1
 
0.2%
-0.9459454058 1
 
0.2%
-0.927377814 1
 
0.2%
-0.8902426304 1
 
0.2%
-0.834539855 2
 
0.5%
-0.741701896 6
 
1.4%
-0.7231343042 1
 
0.2%
-0.6674315287 3
 
0.7%
-0.6358666227 1
 
0.2%
ValueCountFrequency (%)
4.680034911 1
0.2%
3.937331239 1
0.2%
3.046086832 1
0.2%
2.804708139 1
0.2%
2.786140547 2
0.5%
2.563329445 1
0.2%
2.414788711 1
0.2%
2.229112793 1
0.2%
2.191977609 1
0.2%
2.154842425 1
0.2%

aggregate_coarse_kg
Real number (ℝ)

Distinct283
Distinct (%)67.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0024822075
Minimum-1.8524941
Maximum2.2572799
Zeros0
Zeros (%)0.0%
Negative212
Negative (%)50.6%
Memory size6.5 KiB
2025-04-24T07:49:47.399755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1.8524941
5-th percentile-1.5777127
Q1-0.8752281
median-0.031768665
Q30.68923384
95-th percentile1.6981595
Maximum2.2572799
Range4.109774
Interquartile range (IQR)1.5644619

Descriptive statistics

Standard deviation1.0005149
Coefficient of variation (CV)403.07463
Kurtosis-0.83127947
Mean0.0024822075
Median Absolute Deviation (MAD)0.81837069
Skewness0.10040288
Sum1.0400449
Variance1.00103
MonotonicityNot monotonic
2025-04-24T07:49:47.467814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.2874348387 9
 
2.1%
0.1307107717 9
 
2.1%
-1.242001532 7
 
1.7%
1.086472167 6
 
1.4%
0.5249623473 5
 
1.2%
0.5488563822 5
 
1.2%
-1.02814992 5
 
1.2%
0.1426577892 4
 
1.0%
-1.601606757 4
 
1.0%
-1.577712722 4
 
1.0%
Other values (273) 361
86.2%
ValueCountFrequency (%)
-1.852494124 4
1.0%
-1.851299422 1
 
0.2%
-1.847715317 1
 
0.2%
-1.733023949 2
0.5%
-1.697182897 1
 
0.2%
-1.695988195 1
 
0.2%
-1.650589529 1
 
0.2%
-1.649394827 1
 
0.2%
-1.63744781 2
0.5%
-1.635058406 1
 
0.2%
ValueCountFrequency (%)
2.257279876 1
 
0.2%
2.12944679 1
 
0.2%
2.078074615 1
 
0.2%
2.018339528 4
1.0%
2.011171317 2
0.5%
1.95860444 2
0.5%
1.946657423 1
 
0.2%
1.944268019 1
 
0.2%
1.934710405 1
 
0.2%
1.874975318 1
 
0.2%

aggregate_fine_kg
Real number (ℝ)

Distinct302
Distinct (%)72.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.0078212578
Minimum-2.3328404
Maximum3.1248926
Zeros0
Zeros (%)0.0%
Negative201
Negative (%)48.0%
Memory size6.5 KiB
2025-04-24T07:49:47.535399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-2.3328404
5-th percentile-1.7618734
Q1-0.71715427
median0.067411968
Q30.62810983
95-th percentile1.6429798
Maximum3.1248926
Range5.4577331
Interquartile range (IQR)1.3452641

Descriptive statistics

Standard deviation1.0014506
Coefficient of variation (CV)-128.04214
Kurtosis-0.22092385
Mean-0.0078212578
Median Absolute Deviation (MAD)0.6750282
Skewness-0.079738466
Sum-3.277107
Variance1.0029033
MonotonicityNot monotonic
2025-04-24T07:49:47.608062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1317655686 5
 
1.2%
-2.332840427 5
 
1.2%
0.2139191018 4
 
1.0%
0.816378345 4
 
1.0%
0.4877642123 4
 
1.0%
-0.7171542742 4
 
1.0%
-0.2653098417 4
 
1.0%
0.55622549 4
 
1.0%
1.103915711 4
 
1.0%
0.5014564679 4
 
1.0%
Other values (292) 377
90.0%
ValueCountFrequency (%)
-2.332840427 5
1.2%
-2.182225616 1
 
0.2%
-2.089118278 1
 
0.2%
-2.086379827 1
 
0.2%
-2.072687571 3
0.7%
-2.06994912 2
 
0.5%
-2.058995316 1
 
0.2%
-1.935765016 2
 
0.5%
-1.839919227 2
 
0.5%
-1.826226972 1
 
0.2%
ValueCountFrequency (%)
3.124892627 1
0.2%
2.473141264 1
0.2%
2.447125978 1
0.2%
2.432064497 1
0.2%
2.208880732 1
0.2%
1.937774073 1
0.2%
1.908883414 1
0.2%
1.906144963 1
0.2%
1.881635825 1
0.2%
1.869312795 1
0.2%

age_days
Categorical

Constant 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
0.0
419 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1257
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 419
100.0%

Length

2025-04-24T07:49:47.676894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-24T07:49:47.711055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 419
100.0%

Most occurring characters

ValueCountFrequency (%)
0 838
66.7%
. 419
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1257
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 838
66.7%
. 419
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1257
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 838
66.7%
. 419
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1257
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 838
66.7%
. 419
33.3%

compressive_strength_mpa
Real number (ℝ)

High correlation 

Distinct402
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.021713767
Minimum-1.9200335
Maximum3.0626797
Zeros0
Zeros (%)0.0%
Negative228
Negative (%)54.4%
Memory size6.5 KiB
2025-04-24T07:49:47.761364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1.9200335
5-th percentile-1.4513234
Q1-0.71693748
median-0.2054809
Q30.50750834
95-th percentile1.8686867
Maximum3.0626797
Range4.9827132
Interquartile range (IQR)1.2244458

Descriptive statistics

Standard deviation0.98034873
Coefficient of variation (CV)-45.148718
Kurtosis0.23084246
Mean-0.021713767
Median Absolute Deviation (MAD)0.58184051
Skewness0.64112574
Sum-9.0980683
Variance0.96108363
MonotonicityNot monotonic
2025-04-24T07:49:47.840958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.7873214082 3
 
0.7%
-0.9740734435 2
 
0.5%
0.6761951699 2
 
0.5%
0.9713384619 2
 
0.5%
1.279620088 2
 
0.5%
0.04602435734 2
 
0.5%
-1.307224059 2
 
0.5%
1.051576145 2
 
0.5%
-0.2082962535 2
 
0.5%
0.3144217548 2
 
0.5%
Other values (392) 398
95.0%
ValueCountFrequency (%)
-1.920033502 1
0.2%
-1.919986579 1
0.2%
-1.83838814 1
0.2%
-1.838200449 1
0.2%
-1.783957898 1
0.2%
-1.783910975 1
0.2%
-1.672282058 1
0.2%
-1.6719536 1
0.2%
-1.667589796 1
0.2%
-1.653043783 1
0.2%
ValueCountFrequency (%)
3.062679721 1
0.2%
2.942557809 1
0.2%
2.8618509 1
0.2%
2.687298746 1
0.2%
2.6023688 1
0.2%
2.582661299 1
0.2%
2.569053739 1
0.2%
2.405763014 1
0.2%
2.398255395 1
0.2%
2.351332773 1
0.2%

Interactions

2025-04-24T07:49:46.056355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T07:49:42.637751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T07:49:42.986116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T07:49:43.378511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T07:49:43.852724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T07:49:44.399017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T07:49:44.962252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T07:49:45.450464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T07:49:46.105625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T07:49:42.684729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T07:49:43.025264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T07:49:43.433457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T07:49:43.910605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T07:49:44.469173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T07:49:45.025193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T07:49:45.508182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T07:49:46.156416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T07:49:42.725785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T07:49:43.066628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T07:49:43.489849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T07:49:43.966341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T07:49:44.527689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T07:49:45.088570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T07:49:45.564568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T07:49:46.217135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T07:49:42.772613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T07:49:43.116242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T07:49:43.548414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T07:49:44.033238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T07:49:44.599864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T07:49:45.151662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T07:49:45.624695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T07:49:46.271202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T07:49:42.803978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T07:49:43.155092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-04-24T07:49:45.214808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T07:49:45.684648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T07:49:46.316205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-04-24T07:49:45.271146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T07:49:45.732939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T07:49:46.372415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T07:49:42.886928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T07:49:43.266672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T07:49:43.716703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T07:49:44.245948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T07:49:44.817802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T07:49:45.329509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T07:49:45.946134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T07:49:46.417529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T07:49:42.939014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T07:49:43.316772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T07:49:43.783312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T07:49:44.319584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T07:49:44.881959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T07:49:45.391018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T07:49:45.988943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-04-24T07:49:47.892236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
additive_kgaggregate_coarse_kgaggregate_fine_kgcement_kgcompressive_strength_mpascm1_kgscm2_kgwater_kg
additive_kg1.000-0.2870.069-0.0880.1840.0390.497-0.457
aggregate_coarse_kg-0.2871.000-0.1490.030-0.143-0.265-0.231-0.299
aggregate_fine_kg0.069-0.1491.000-0.123-0.188-0.272-0.050-0.287
cement_kg-0.0880.030-0.1231.0000.647-0.378-0.394-0.139
compressive_strength_mpa0.184-0.143-0.1880.6471.0000.154-0.223-0.350
scm1_kg0.039-0.265-0.272-0.3780.1541.000-0.1970.096
scm2_kg0.497-0.231-0.050-0.394-0.223-0.1971.000-0.079
water_kg-0.457-0.299-0.287-0.139-0.3500.096-0.0791.000

Missing values

2025-04-24T07:49:46.501284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-24T07:49:46.566612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

cement_kgscm1_kgscm2_kgwater_kgadditive_kgaggregate_coarse_kgaggregate_fine_kgage_dayscompressive_strength_mpa
02.626147-0.983607-0.949284-1.090769-0.834541.002843-1.2100750.02.942558
12.626147-0.983607-0.949284-1.090769-0.834541.182048-1.2100750.01.710839
21.0957390.099347-0.9492842.327744-1.29873-0.287435-2.3328400.0-0.020465
30.0053240.315937-0.9492842.327744-1.29873-0.287435-1.2922290.00.619700
42.004419-0.983607-0.9492842.327744-1.29873-0.287435-2.3328400.00.172950
5-0.6393600.525689-0.9492840.463101-1.298730.2669070.8369170.0-0.593906
60.368796-0.117244-0.9492842.327744-1.29873-0.287435-1.2922290.00.753054
7-1.2036981.403451-0.9492840.463101-1.298731.0864720.5822410.0-0.579220
81.550079-0.442130-0.9492842.327744-1.29873-0.287435-2.3328400.00.046212
9-0.2672801.723778-0.9492842.327744-1.29873-0.287435-2.3328400.0-0.453842
cement_kgscm1_kgscm2_kgwater_kgadditive_kgaggregate_coarse_kgaggregate_fine_kgage_dayscompressive_strength_mpa
4150.2195810.395734-0.949284-0.2931160.001002-0.5753580.8916860.00.366928
4160.313319-0.9836070.6682631.3798840.762273-0.913459-0.2762640.0-0.331656
417-0.0090240.2817390.3583590.644385-0.203242-1.4749680.3563190.00.326246
418-1.0104841.866272-0.949284-0.7592770.9665171.113950-1.0430300.00.184258
419-0.9511811.976847-0.9492840.0072991.059355-1.1619570.8547170.00.079527
4200.1048010.3387370.415805-0.1791650.353786-1.0269550.0537200.00.512858
4210.542880-0.9836070.7982710.6702830.632300-1.6505900.6712400.0-0.379048
422-1.1185690.6054850.6924500.499358-0.166107-0.7605370.2139190.0-0.888252
423-1.0171801.144682-0.949284-0.3863480.7994080.4007130.3357800.0-0.270891
424-0.0434580.1620440.2343970.9085430.298083-1.093859-0.0393880.0-0.295854